Emotion Detection

  To create a complete project on Face Recognition, we must work on 3 very distinct phases: Face Detection and Data Gathering Train the Recognizer Face Recognition The below block diagram resumes those phases:

  The most basic task on Face Recognition is of course, “Face Detecting”. Before anything, you must “capture” a face (Phase 1) in order to recognize it, when compared with a new face captured on future (Phase 3). The most common way to detect a face (or any objects), is using the “ Haar Cascade classifier ” Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. The good news is that OpenCV comes with a trainer as well...